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    Author(s): John W. CoulstonGretchen G. MoisenBarry T. Wilson; Mark V. Finco; Warren B. CohenC. Kenneth Brewer
    Date: 2012
    Source: Photogrammetric Engineering & Remote Sensing 78(7): 715–727
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Southwest Research Station
    PDF: View PDF  (758.05 KB)

    Description

    Tree canopy cover is a fundamental component of the landscape, and the amount of cover influences fire behavior, air pollution mitigation, and carbon storage. As such, efforts to empirically model percent tree canopy cover across the United States are a critical area of research. The 2001 national-scale canopy cover modeling and mapping effort was completed in 2006, and here we present results from a pilot study for a 2011 product. We examined the influence of two different modeling techniques (random forests and beta regression), two different Landsat imagery normalization processes, and eight different sampling intensities across five different pilot areas. We found that random forest out-performed beta regression techniques and that there was little difference between models developed based on the two different normalization techniques. Based on these results we present a prototype study design which will test canopy cover modeling approaches across a broader spatial scale.

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    Citation

    Coulston, John W.; Moisen, Gretchen G.; Wilson, Barry T.; Finco, Mark V.; Cohen, Warren B.; Brewer, C. Kenneth. 2012. Modeling percent tree canopy cover: a pilot study. Photogrammetric Engineering & Remote Sensing 78(7): 715–727.

    Keywords

    landsat, remote sensing, tree cover

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